The spectral correlation density (SCD) is widely used to characterize cyclostationary signals and the strip spectral correlation analyzer (SSCA) is commonly used to estimate the SCD. Although the SSCA utilizes the fast Fourier transform (FFT) for computational efficiency, its real-time implementation still poses challenges as large input sizes are often involved. In this work, we present a sparse strip spectral correlation analyzer (S3CA) based on the sparse fast Fourier transform (SFFT). The S3CA approach involves computing a sparse, downsampled channel-data product (CDP) which is then passed to a modified SFFT implementation to obtain the spectral density. For an input of length 2 million samples, the S3CA is 30× faster than the conventional SSCA.

The RF environment in a secure space can be compromised by intentional transmissions of hard-to-detect underlay signals that overlap with a high-power baseline transmission. Specifically, we consider the case where a direct sequence spread spectrum (DSSS) signal is the underlay signal hiding within a baseline 4G Long-Term Evolution (LTE) signal. As compared to overt actions like jamming, the DSSS signal allows the LTE signal to be decodable, which makes it hard to detect. ICARUS presents a machine learning based framework that offers choices at the physical layer for inference with inputs of (i) in-phase and quadrature (IQ) samples only, (ii) cycle-frequency features obtained via cyclostationary signal processing (CSP), and (iii) fusion of both, to detect the underlay DSSS signal and its modulation type within LTE frames. ICARUS chooses the best inference method considering both the expected accuracy and the computational overhead. ICARUS is rigorously validated on multiple real-world datasets that include signals captured in cellular bands in the wild and the NSF POWDER testbed for advanced wireless research (PAWR). Results reveal that ICARUS can detect DSSS anomalies and its modulation scheme with 98-100% and 67 − 99% accuracy, respectively, while completing inference within 3 − 40 milliseconds on an NVIDIA A100 GPU platform.

The paper considers the problem of deep-learning-based classification of digitally modulated signals using I/Q data and studies the generalization ability of a trained neural network (NN) to correctly classify digitally modulated signals it has been trained to recognize when the training and testing datasets are distinct. Specifically, we consider both a residual network (RN) and a convolutional neural network (CNN) and use them in conjunction with two different datasets that contain similar classes of digitally modulated signals but that have been generated independently using different means, with one dataset used for training and the other one for testing.

Dynamic spectrum sharing paradigm is envisaged to meet the growing demand for the Radio Frequency (RF) spectrum. There exist several technical, regulatory, and business impediments for adopting the new paradigm. To address these challenges, we need the ability to characterize and quantify the use of spectrum. In this paper, we propose MUSE, a methodology to characterize and quantify the use of spectrum in the space, time, and frequency dimensions. MUSE characterizes the use of spectrum by a transmitter at a point in terms of the RF power occupied by the transmitter. It characterizes the use of spectrum by a receiver at a point in terms of the constraints on the RF-power that can be occupied by any of the transmitters in the system in order to ensure successful reception. It divides the spectrum-space into discrete unit-spectrum-spaces and quantifies the spectrum used by the individual transceivers in the discretized spectrum space. To bring in simplicity, precision, and efficiency to dynamic spectrum sharing, we emphasize on articulating, defining, and enforcing the quantified spectrum-access rights in the discretized spectrum-space.

Previous studies on the cyclostationarity aspect of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals assumed simplified signal and channel models or considered only second-order cyclostationarity. This paper presents new results concerning the cyclostationarity of these signals under more general conditions, including time dispersive channels, additive Gaussian noise, and carrier phase, frequency, and timing offsets. Analytical closed-form expressions are derived for time- and frequency-domain parameters of the cyclostationarity of OFDM and SCLD signals. In addition, a condition to eliminate aliasing in the cycle and spectral frequency domains is derived. Based on these results, an algorithm is developed for recognizing OFDM versus SCLD signals. This algorithm obviates the need for commonly required signal preprocessing tasks, such as signal and noise power estimation and the recovery of symbol timing and carrier information.

Cognitive radios opportunistically share the spectrum with the primary users. In this paper, we address the problem of detecting the presence of a primary user while a secondary user is transmitting data, in order to avoid harmful interference to the primary user. We have proposed an inband spectrum sensing technique utilizing the OFDM guard interval for scanning the spectrum. The spectrum sensing data from the discontiguous OFDM guard intervals is combined to obtain a long coherent data segment. We have shown that cyclostationarity can be exploited for the user detection even when the sensing data is discontiguous. The 'listen-while-talking' technique reliably detects the ATSC DTV signal in 8.5 ms when SNR is -21 dB. The inband spectrum sensing technique provides protection to primary users and saves the system capacity compared to the conventional system which uses idle period.

Cognitive radio (CR) is believed to be an enabling technology for increasing spectrum efficiency. A CR collects spectrum usage information from not only its own spectrum sensing module, but also from peer CRs. The heavy dependence on spectrum knowledge from other CRs requires identification of malicious CR devices that could generate spoofed information. In addition, it also needs to track the users associated with problematic CR devices which unintentionally violate spectrum usage etiquette. The specific emitter identification (SEI) concept is applied to identification of such non-cooperative CR devices. In this paper, second-order cyclic features of OFDM signals are proposed as a means of increasing CR network security and stability through SEI. For this exploratory work, IEEE 802.11a/g signals from different WLAN cards are measured and classified using hidden Markov Models (HMMs).

The problem of detecting the presence of direct sequence spread-spectrum signals in noise is considered, and the detector consisting of a filter followed by a delay-and-multiply device is optimized with respect to the filter bandwidth and the delay to maximize robustness to errors in assumed values of pulse rate and carrier frequency. Output SNR is used for performance optimization, and the results are corroborated by evaluating receiver operating characteristics.>

The problem of dual-receiver interception of low-SNR signals, which includes detection of the presence of a particular signal type and location of its source, is considered. In particular, source-location based on time-difference-of-arrival (TDOA) measurements is examined, and the low-SNR maximum-likelihood receiver for joint detection and TDOA estimation is taken as a starting point. By explicitly revealing the way in which this receiver exploits the spectral correlation properties of the cyclostationary signal, several partial implementations with optimality properties of their own are proposed. These greatly simplified implementations, which require only a one-dimensional search over the TDOA parameter, are shown by simulation to perform competitively with the relatively complicated maximum-likelihood receiver which requires a two- or three-dimensional search for phase-shift keyed (PSK) signals.>

The problem of detecting the presence of spread-spectrum phase-shift-keyed signals in variable noise and interference backgrounds is considered, and the performances of four detectors are evaluated and compared. The detectors include the optimum radiometer, the optimum modified radiometer that jointly estimates the noise level and detects the signal, and the maximum-SNR spectral-line regenerator for spectral-line frequencies equal to the chip rate and the doubled carrier frequency. It is concluded that the spectral-line regenerators can outperform both types of radiometers by a wide margin. The performance advantages are quantified in terms of receiver operating characteristics for several noise and interference environments and receiver collection times.>